Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process

Developing integrated environmental models of everywhere such as are demanded by the requirements of, for example, implementing the Water Framework Directive in Europe, is constrained by the limitations of current understanding and data availability. The possibility of such models raises questions about system design requirements to allow modelling as a learning and data assimilation process in the representation of places, which might well be treated as active objects in such a system. Uncertainty in model predictions not only poses issues about the value of different types of data in characterising places and constraining predictive uncertainty but also about how best to present the pedigree of such uncertain predictions to users and decision-makers.

[1]  John Ewen,et al.  VALIDATION OF CATCHMENT MODELS FOR PREDICTING LAND-USE AND CLIMATE CHANGE IMPACTS. : 2. CASE STUDY FOR A MEDITERRANEAN CATCHMENT , 1996 .

[2]  R. T. Clarke,et al.  A distribution function approach to modelling basin sediment yield , 1983 .

[3]  Keith Beven,et al.  Uniqueness of place and process representations in hydrological modelling , 2000 .

[4]  J. R. Blackie,et al.  Lumped catchment models , 1985 .

[5]  Howard S. Wheater,et al.  Estimation and propagation of parametric uncertainty in environmental models , 2002 .

[6]  Peter C. Young,et al.  Top‐down and data‐based mechanistic modelling of rainfall–flow dynamics at the catchment scale , 2003 .

[7]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[8]  Keith Beven,et al.  Sensitivity analysis, calibration and predictive uncertainty of the Institute of Hydrology Distributed Model , 1985 .

[9]  Keith Beven,et al.  Modelling the Chloride Signal at the Plynlimon Catchments, Wales Using a Modified Dynamic TOPMODEL. , 2007 .

[10]  Jens Christian Refsgaard,et al.  Methodology for construction, calibration and validation of a national hydrological model for Denmark , 2003 .

[11]  Stefan Uhlenbrook,et al.  On the value of experimental data to reduce the prediction uncertainty of a process-oriented catchment model , 2005, Environ. Model. Softw..

[12]  S. Funtowicz,et al.  Science for the Post-Normal Age , 1993, Commonplace.

[13]  Paul D. Bates,et al.  Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE , 2002 .

[14]  R. Moore,et al.  A distribution function approach to rainfall runoff modeling , 1981 .

[15]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[16]  Mary P. Anderson,et al.  The role of the postaudit in model validation , 1992 .

[17]  A. Calver DEVELOPMENT AND EXPERIENCE OF THE TATE RAINFALL RUNOFF MODEL. , 1996 .

[18]  W. L. Wood,et al.  On the discretization and cost-effectiveness of a finite element solution for hillslope subsurface flow , 1989 .

[19]  Peter C. Young,et al.  Data-based mechanistic modelling and the simplification of environmental systems. , 2004 .

[20]  Keith Beven,et al.  The Institute of Hydrology distributed model , 1987 .

[21]  Keith Beven Towards environmental models of everywhere: advances in modelling and data assimilation. , 2004 .

[22]  Richard P. Hooper,et al.  Assessing the Birkenes Model of stream acidification using a multisignal calibration methodology , 1988 .

[23]  George H. Leavesley,et al.  A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling , 2002 .

[24]  John D. Bredehoeft,et al.  Ground-water models cannot be validated , 1992 .

[25]  P. Young,et al.  Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools? , 1998 .

[26]  Paul Whitehead,et al.  Quality simulation along rivers (QUASAR): an application to the Yorkshire Ouse , 1997 .

[27]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[28]  Keith Beven,et al.  On the role of physically-based distributed modelling in hydrology , 1982 .

[29]  K. Beven Towards a coherent philosophy for modelling the environment , 2002, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[30]  James C. Bathurst,et al.  Physically-based distributed modelling of an upland catchment using the Systeme Hydrologique Europeen , 1986 .

[31]  Justin L. Huntington,et al.  Stochastic capture zone analysis of an arsenic-contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology , 2003 .

[32]  H. Wheater,et al.  Subsurface flow simulation of a small plot at loch chon, Scotland , 1992 .

[33]  K Beven,et al.  On the concept of model structural error. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[34]  J. Kirchner,et al.  Fractal stream chemistry and its implications for contaminant transport in catchments , 2000, Nature.

[35]  Keith Beven,et al.  Distributed Hydrological Modelling , 1998 .

[36]  Keith Beven,et al.  Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .

[37]  R. Moore The probability-distributed principle and runoff production at point and basin scales , 1985 .

[38]  Keith Beven,et al.  Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations , 2001 .

[39]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[40]  K. Beven,et al.  SHE : towards a methodology for physically-based distributed forecasting in hydrology , 2007 .

[41]  Leonard A. Smith,et al.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.

[42]  Jan Feyen,et al.  Constraining soil hydraulic parameter and output uncertainty of the distributed hydrological MIKE SHE model using the GLUE framework , 2002 .

[43]  Keith Beven,et al.  CHANGING RESPONSES IN HYDROLOGY : ASSESSING THE UNCERTAINTY IN PHYSICALLY BASED MODEL PREDICTIONS , 1991 .

[44]  A. Calver,et al.  Calibration, sensitivity and validation of a physically-based rainfall-runoff model , 1988 .